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Posted 14 June, 2026

Senior AI Cloud Engineer (AWS Bedrock & Generative AI)

Enexus Global Inc.
Pune District, MH, IN Full Time
Reference: 18666eff60d67b66

Job Description

Job Title: Senior AI Cloud Engineer (AWS & Generative AI)

Location: Pune, India

Role Type: Contract


Role Overview

We are seeking a highly technical Cloud Engineer to build and optimize our Generative AI infrastructure. This role focuses on deploying AWS Bedrock agents, automating workflows with Python , and creating robust observability and billing pipelines for LLM usage. You will be responsible for ensuring that our AI services are not only functional but also cost-effective and highly monitored through advanced logging and alerting.

Key Responsibilities

  • AI Agent Orchestration: Design and deploy specialized agents using AWS Agents for Amazon Bedrock to automate complex multi-step business processes.
  • Observability & Alerting: Build end-to-end "Data Log" pipelines. Identify and implement the correct AWS services for alerting (e.g., CloudWatch, SNS, or Lambda) based on log anomalies.
  • Integration & Middleware: Manage and analyze Mulesoft logs to ensure seamless connectivity between legacy systems and modern AI services.
  • Financial Operations (FinOps): Monitor billing metrics for LLM usage and AWS Bedrock services to prevent cost overruns and optimize token consumption.
  • Python Automation: Write production-grade Python scripts for data processing, agent logic, and infrastructure automation.

Technical Requirements (The "Must-Haves")

  • Core AWS AI Services: Hands-on experience with AWS Bedrock and AWS Agent Core logic.
  • Programming: High proficiency in Python (specifically for data manipulation and API integrations).
  • Logging & Monitoring: Deep understanding of log aggregation. Experience with Mulesoft logs is a significant plus.
  • Alerting Frameworks: Ability to determine which AWS service to use for specific alerts (CloudWatch Alarms vs. EventBridge vs. Managed Grafana).
  • LLM Knowledge: Understanding of how LLMs work, including tokenization, prompt engineering, and the cost structure of different models.

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